Glean vs Dust: Enterprise AI Search and Assistants Compared
Glean and Dust both promise work-grounded AI, but serve different operators. Here's how connectors, grounding quality, and pricing stack up.
The “work-grounded AI” category has split into two distinct product shapes. One is the deep-index enterprise search platform—crawl everything, make it findable, answer from it. The other is the multiplayer agent workspace—connect your sources, build agents per team or function, share context across the org. Glean leads the first shape; Dust leads the second. Understanding which problem you actually have will save you months of wasted procurement.
What Each Product Actually Does
Glean is an enterprise AI search platform that combines workplace search, a conversational AI assistant, and autonomous agent capabilities across 100+ enterprise applications. The company has evolved from a search tool to a Work AI platform featuring hybrid search methodology (keyword + vector + RAG) and a dual-graph architecture built around Enterprise and Personal knowledge graphs.
Dust takes a different angle. Dust empowers teams to create agents that actually understand company context, fully customized to match how you actually work—deploying everything from simple workflows to complex enterprise integrations. The framing is explicitly “multiplayer”: one person prompts an assistant, gets an answer, and the context disappears into a private chat window—resulting in real productivity at the individual level with very little compounding across teams. Dust was built to change that by making AI collaborative, shared, and operational across an entire company.
The bottom line on product identity: Glean is primarily a search and retrieval layer with assistant features bolted on. Dust is primarily an agent-building and collaboration platform with retrieval as infrastructure underneath.
Connectors and Data Grounding
Both platforms advertise 100+ connectors, but the architectures behind them are meaningfully different.
Glean integrates 100+ apps across ecosystems out of the box, with flexible content controls to decide what data the AI uses. Glean analyzes content, activity, and people to understand how you work—with all data permissions inherited and strictly enforced, so users only see what they’re allowed to. Data updates as soon as it changes in the source application, including permissioning rules, which are reflected immediately in results.
That last point—real-time permission propagation—is Glean’s core grounding strength. Glean has over 100 built-in connectors that eliminate the need for custom development, which often slows enterprise integrations. Each connector uses official APIs to retrieve documents, messages, and metadata while respecting permission boundaries. Administrators can manage integrations through a clean interface without handling rate limits, webhooks, or middleware.
The tradeoff is architectural weight. Because Glean relies on centralized indexing, the platform continuously processes enterprise data from connected tools. That continuous full-index crawl is what makes search quality excellent—and what makes infrastructure costs steep (more on that below).
Dust’s connector story is similar in breadth— the platform connects to more than 100 enterprise data sources, including Notion, Salesforce, GitHub, Slack, and Google Drive. But Dust is leaning harder into MCP as its extensibility layer. MCP (Model Context Protocol) is an open standard for connecting AI agents to external data sources and tools; Dust’s MCP adoption means its agents can use the same connection layer as other MCP-compatible platforms. Recent changelog entries show this in practice: the Confluence MCP Server is now available to all Dust workspaces, allowing agents to interact directly and dynamically with Confluence—reading, searching, and even writing content from within Dust.
Dust’s granularity controls are notable for smaller or more sensitive teams: connections are managed by workspace admins, who have granular control over what data Dust can access—for example, which Slack channels, Google Drive folders, or Notion pages. One documented limitation worth knowing: GitHub integration only gathers data from issues, discussions, and top-level pull request comments—not in-code comments or actual source code.
For teams building custom integrations or reaching legacy systems, Dust connects natively to modern SaaS tools and can reach legacy systems through REST APIs and middleware layers such as Zapier or Make, though it is primarily optimized for modern cloud-based systems. Glean similarly offers a developer-facing push API: you can bring custom, permission-aware data into Glean alongside its 100+ out-of-the-box connectors.
Grounding Quality: Retrieval vs. Agent Context
This is where the products diverge most. Glean’s hybrid search (keyword + vector + permission-aware graph) is purpose-built for retrieval precision across large, messy corpora. When an employee asks “what’s our current refund policy?” Glean will find the right Confluence page, respect the user’s read permissions, and answer from it—no prompt engineering required.
Dust’s grounding quality depends heavily on how well the workspace admin has configured each agent’s data scope. The default @dust agent explores all synchronized data—but you shouldn’t expect 100% accurate answers; use Dust as a router to navigate your knowledge. That’s an honest acknowledgment that Dust is optimized for agent workflows with bounded, well-defined knowledge sets rather than org-wide open-ended search.
Glean’s retrieval model also personalizes results: the dual-graph architecture—Enterprise Graph and Personal Graph—means Glean understands both organizational knowledge and individual work patterns to improve result relevance over time.
If your primary pain is “our people can’t find things across a fragmented stack,” Glean’s search precision edge matters a lot. If your pain is “we need agents that take action on the knowledge they find,” Dust’s agent-building surface is the more practical path. Operators evaluating adjacent agent orchestration tools may also want to see our coverage of Relevance AI and n8n for workflow automation that sits outside the knowledge search layer.
Model Flexibility and Agent Capabilities
Dust lets you choose OpenAI, Anthropic, Gemini, Mistral, or any cutting-edge model to ensure your agents stay current—they believe in options. That model-agnosticism is a real differentiator for teams that want to swap in Claude 3.7 or Gemini 1.5 Pro without a vendor dependency.
Glean is more opinionated. You can build agents in your preferred framework—OpenAI, LangChain, Google ADK, CrewAI—all grounded in the full context of your enterprise. Glean also runs in MCP-enabled IDEs and apps like Cursor, VS Code, Windsurf, and Claude Desktop. The agent story is still search-centric—the agents run on top of the index—whereas Dust agents can be configured to act across tools, write back to systems, and orchestrate multi-step workflows. Teams doing heavy agent development should also look at our Clay review for GTM-specific agent workflows that layer on top of either platform’s data grounding.
Dust’s multiplayer angle is now backed by real traction: the company serves more than 3,000 organizations with 51,000 monthly active users, zero churn in 2025, and 300,000 agents deployed across the platform. That last number is significant—it signals teams are actually shipping agents, not just demos.
Pricing and TCO: The Honest Numbers
Glean does not publish pricing. Enterprise pricing in 2026 starts at approximately $50+ per user/month, with a minimum enterprise contract of approximately 100 seats (~$60,000/year ACV). All contracts are custom-quoted through a direct sales process.
The per-seat number is only part of the story. Glean operates a hybrid pricing model combining per-user seat licensing ($45–50/user/month base + $15/user/month AI add-on) with consumption-based FlexCredits for premium features that meter compute-intensive capabilities beyond included thresholds. And infrastructure adds up fast: a documented 20-user proof of concept required 26 high-memory compute nodes on Google Cloud Platform, generating cloud spend exceeding $10,000 per month before any licensing fees; for a mid-to-large enterprise deployment, cloud infrastructure typically adds $120,000 or more per year.
Dust is transparently priced. The Pro plan runs €29 per user/month (excluding tax), covering advanced models, custom agents, key connections, unlimited messages (fair-use), and up to 1 GB/user of data sources.
The Enterprise plan targets 100+ users with custom pricing, adding SSO, larger storage, SCIM provisioning, regional hosting, and priority support. Both plans include a 14-day free trial. Per-seat pricing can add up when you want org-wide access; the 1 GB/user cap on data sources in Pro may also push some buyers to Enterprise earlier than expected.
The pricing gap is significant. Glean is fundamentally a large-enterprise procurement; Dust can be evaluated and deployed by a lean ops team without a six-figure commitment.
When to Pick Glean
- You have 500+ employees across a sprawling SaaS stack and knowledge fragmentation is your primary problem
- Your security team requires permission-aware, real-time search across every source—not just document retrieval
- You’re in a heavily compliance-sensitive environment and want a battle-tested enterprise vendor with deep Microsoft/Google ecosystem integrations
- You have budget, dedicated IT resources for deployment, and time for a proper sales and implementation cycle
When to Pick Dust
- You want to build department-specific agents (sales, support, engineering, HR) that act on company knowledge—not just retrieve it
You’ve adopted the Model Context Protocol and want enterprise data connectivity with governance controls that let every team run AI-powered workflows without losing control of what the AI can access
- Your team is 50–500 people and you need a self-serve path without a six-figure minimum commitment
- Model flexibility matters and you don’t want to be locked into one LLM provider’s context window
Bottom line: Glean is the right call if your primary need is enterprise-grade, permission-aware search at scale across a sprawling tool ecosystem—and you have the budget and IT infrastructure to support it. Dust is the better fit for operators who want to build and govern shared AI agents across functions, with model flexibility and a pricing model accessible to companies that aren’t Fortune 500s. The two products are beginning to overlap at the edges (both now advertise 100+ connectors and agent capabilities), but their core architectures—centralized index vs. multiplayer agent workspace—will continue to favor different use cases for the foreseeable future.